Density-dependent population dynamics of mountain pine beetle in thinned and unthinned stands
Bibliographic record
Abstract
Thinning, the selective removal of some trees from a forest, is one way forest managers can reduce the probability that a forest will be susceptible to attack by bark beetles. Although this method has been shown to be effective, it is not clear whether the effect arises when pre-outbreak populations are small or during the epidemic phase when outbreaks are growing. We adopted a population dynamics approach to determine if the effect of limit or basal area thinning could be observed in the form of differential beetle recruitment using lodgepole pine ( Pinus contorta Dougl. ex Loud.) and ponderosa pine ( Pinus ponderosa Dougl. ex P. & C. Laws.) mortality data from previously published studies as a proxy measure of mountain pine beetle ( Dendroctonus ponderosae Hopkins) population size. We found that mountain pine beetle populations exhibit density-dependent population dynamics that are influenced by the silvicultural history of their host’s stand. Thinning did not change the epidemic equilibrium but instead caused a shift in dynamics from linear to nonlinear. In a validation test, the models developed for thinned and unthinned stands predicted reproductive rates in independent locations. These data also suggest the epidemic dynamics of mountain pine beetle may be sensitive to perturbations and to systematic trends associated with climate variability and climate change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".